Abstract
Within the context of graph theory, a topological index serves as a numerical descriptor that encapsulates the physicochemical properties of a chemical graph. These are particularly useful in cheminformatics, where they serve as a compact representation of the molecule's structure, capturing various physicochemical properties such as molecular size, shape, branching, and connectivity. These studies are pivotal in the initial phases of drug development, facilitating the identification and optimization of potential pharmaceutical drugs. In this paper, we discuss a range of distance-based topological indices applied to a selection of tricyclic anti-depressant drugs aiming to understand their physicochemical characteristics. Additionally, the quantitative structure-property relationship (QSPR) analysis is explored for distance-based topological indices aim to predict how changes in chemical structure might influence the efficacy and potency of these drugs.